Week 02

Week 02:
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Topic: Probability & Supervised Learning I

Objectives:

Define learning in the context of artificial intelligence
Define supervised and unsupervised learning and identify when supervised and unsupervised learning should be used
Define and give examples of regression and classification in supervised learning and identify when regression or classification should be used
Describe and use various supervised and unsupervised learning algorithms and specify the model characteristics in those algorithms:
- Global versus Local
- Hierarchical versus Non-Hierarchical
- Parametric versus Non-Parametric

Study the following presentations:
- Introduction to the Mathematics of Regression, Part 1:
  - Presentation:
    - PDF: MATHEMATICS_OF_REGRESSION_1.PDF
    - PPTX: MATHEMATICS_OF_REGRESSION_1.PPTX
  - Video:
    - MP4:
      - 1D Data (Mean, Variance, & Standard Deviation): MATHEMATICS_OF_REGRESSION_1_1.MP4
      - 2D Data (Covariance): MATHEMATICS_OF_REGRESSION_1_2.MP4
      - Linear Regression: MATHEMATICS_OF_REGRESSION_1_3.MP4
    - WMV:
      - 1D Data (Mean, Variance, & Standard Deviation): MATHEMATICS_OF_REGRESSION_1_1.WMV
      - 2D Data (Covariance): MATHEMATICS_OF_REGRESSION_1_2.WMV
      - Linear Regression: MATHEMATICS_OF_REGRESSION_1_3.WMV
- Introduction to the Mathematics of Classification, Part 1:
  - Presentation:
    - PDF: MATHEMATICS_OF_CLASSIFICATION_1.PDF
    - PPTX: MATHEMATICS_OF_CLASSIFICATION_1.PPTX
  - Video:
    - MP4:
      - Classification using Level Set & Normal Vector: MATHEMATICS_OF_CLASSIFICATION_1_1.MP4
      - Rotation: MATHEMATICS_OF_CLASSIFICATION_1_2.MP4
      - Translation: MATHEMATICS_OF_CLASSIFICATION_1_3.MP4
    - WMV:
      - Classification using Level Set & Normal Vector: MATHEMATICS_OF_CLASSIFICATION_1_1.WMV
      - Rotation: MATHEMATICS_OF_CLASSIFICATION_1_2.WMV
      - Translation: MATHEMATICS_OF_CLASSIFICATION_1_3.WMV
Complete the "3. Probability in AI" and the "5. Machine Learning" units in the Introduction to Artificial Intelligence course from Udacity:
1. Sign In to Udacity
2. Select Introduction to Artificial Intelligence from the Course Catalog
3. Press the "Take the Class" button
4. Watch the videos and take the quizzes in the "3. Probability in AI" unit.
5. Watch the videos and take the quizzes in the "5. Machine Learning" unit.
Alternatively, download the following ZIP file(s) containing Flash Video files:
- 3. Probability in AI
- 5. Machine Learning
Watch the "I. Introduction (Week 1)" and "II. Linear Regression with One Variable (Week 1)" series of videos in Machine Learning with Andrew Ng course from Coursera:
1. Login to Coursera.
2. Click the Explore Courses link at the top of the page.
3. Search for Machine Learning using the search text box.
4. Select the Machine Learning with Andrew Ng.
5. Press the "Preview" button.
6. Watch the following videos in the "I. Introduction (Week 1)" section (40 min):
  - Welcome (7 min)
  - What is Machine Learning? (7 min)
  - Supervised Learning (12 min)
  - Unsupervised Learning (14 min)
7. Watch the following videos in the "II. Linear Regression with One Variable (Week 1)" section (75 min):
  - Model Representation (8 min)
  - Cost Function (8 min)
  - Cost Function - Intuition I (11 min)
  - Cost Function - Intuition II (9 min)
  - Gradient Descent (11 min)
  - Gradient Descent Intuition (12 min)
  - Gradient Descent For Linear Regression (10 min)
  - What's Next (6 min)
Reference: Coursera's Machine Learning with Andrew Ng.

Read the following pages from Artificial Intelligence: A Modern Approach, Third Edition (cited as AIMA):

Required: 48 Pages

Order	Topic	Book	Section	Page Numbers	Pages	Presentations
1.	Quantifying Uncertainty	AIMA	Chapter 13:	Pages 480-503	(24 Pages)	Uncertainty
2.	Learning from Examples	AIMA	Chapter 18 Intro:	Page 693	(1 Page)	Learning from Observations and Machine Learning
3.	Forms of Learning	AIMA	Sections 18.1:	Pages 693–695	(3 Pages)	Learning from Observations and Machine Learning
4.	Supervised Learning	AIMA	Sections 18.2:	Pages 695–697	(3 Pages)	Learning from Observations and Machine Learning
5.	Evaluating and Choosing the Best Hypothesis	AIMA	Section 18.4:	Pages 708–713	(6 Pages)	Learning from Observations and Machine Learning
6.	Univariate Linear Regression	AIMA	Section 18.6.1:	Pages 717–720	(4 Pages)	Learning from Observations and Machine Learning
7.	Nonparametric Regression	AIMA	Section 18.8.4:	Pages 741–744	(4 Pages)	Learning from Observations and Machine Learning
8.	Linear Classifiers with a Hard Threshold	AIMA	Section 18.6.3:	Pages 723–725	(3 Pages)	Learning from Observations and Machine Learning

Optional: 5 Pages

Order	Topic	Book	Section	Page Numbers	Pages	Presentations
1.	The Theory of Learning	AIMA	Section 18.5:	Pages 713–717	(5 Pages)	Learning from Observations and Machine Learning

Reference: University of California, Berkeley's CS 188: Introduction to Artificial Intelligence (Fall 2005) by Stuart Russell
Reference: Stanford University's CS221: Artificial Intelligence: Principles and Techniques (Autumn 2012-2013) by Percy Liang

Complete Assignment 01
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